KMEANS

import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline


plt.style.use('seaborn')

data=pd.read_csv('processed_data.csv')

data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
       'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
       'DaysSinceFirstStay']]

#Choosing optimal clusters using elbow plot:

from sklearn.cluster import KMeans

wcss = []

for k in range(1,10):
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(data1)
    wcss.append(kmeans.inertia_)

wcss

[595518.9947542043,
 450711.5823636649,
 386045.12816301436,
 337874.5909241742,
 305381.0448057603,
 277377.47623965674,
 261209.24443012773,
 245497.25538219645,
 231138.56892098451]

# Visualization of k values:

plt.plot(range(1,10), wcss, color='red',marker='x')
plt.title('Graph of k values and WCSS')
plt.xlabel('k values')
plt.ylabel('wcss values')
plt.show()

from yellowbrick.cluster import KElbowVisualizer
kmeans=KMeans(random_state=42)
visual=KElbowVisualizer(kmeans, k=(1,10))
visual.fit(data1)
visual.poof()

<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>

#GAP statistics:
import scipy

class TWHGapStat(object):
    
    def generate_random_data(self, X):
        
        reference = scipy.random.random_sample(size=(X.shape[0], X.shape[1]))
        return reference
    
    def _fit_cluster(self,X, n_cluster, n_iter=10):
            
        iterations = range(1, n_iter + 1)
        
        ref_inertias = pd.Series(index=iterations)
        
        for iteration in iterations:
            clusterer = KMeans(n_clusters=n_cluster, n_init=3, n_jobs=-1)
            clusterer.fit(X)
            ref_inertias[iteration] = clusterer.inertia_
        
        mean_nertia = ref_inertias.mean()
        
        return mean_nertia
    
    def fit(self,X,max_k):
        
        k_range = range(1,max_k + 1)
        gap_stat = pd.Series(index=k_range)
        
        ref_data = self.generate_random_data(X)
        
        for k in k_range:
            base_clusterer = KMeans(n_clusters=k,n_init = 3, n_jobs = -1)
            base_clusterer.fit(X)
            
            ref_intertia = self._fit_cluster(ref_data,k)
            
            cur_gap = scipy.log(ref_intertia - base_clusterer.inertia_)
            
            gap_stat[k] = cur_gap
        
        return gap_stat

if __name__ == "__main__":
    gap_stat = TWHGapStat()
    gs = gap_stat.fit(data1,10)
    print (gs)

1     13.213672
2     12.916616
3     12.777263
4     12.606560
5     12.497102
6     12.391419
7     12.326836
8     12.261577
9     12.193390
10    12.169244
dtype: float64

plt.plot(range(len(gs)), gs)
plt.show()

from sklearn.metrics import silhouette_score

sc=[]

kmeans=KMeans(n_clusters=2).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=3).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=4).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=5).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=6).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=7).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=8).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=9).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

kmeans=KMeans(n_clusters=10).fit(data1)
score=silhouette_score(data1,kmeans.labels_)
sc.append(score)

len(sc)

9

fig, ax = plt.subplots(figsize=(12, 8))

ax.plot(range(2, 11), sc, color='blue')

ax.set_title('Silhouette Coefficients for K_Means')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('Silhouette Coefficient')

Text(0, 0.5, 'Silhouette Coefficient')

# K value = 4

from yellowbrick.cluster import SilhouetteVisualizer
model = KMeans(4, random_state=42)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(data1)        
visualizer.show()

<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 63670 Samples in 4 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>

# Initializes the model with selected K, fit and predict the labels for the dataset
kmeans = KMeans(n_clusters=4, random_state=42).fit(data1)
labels_km_1 = kmeans.predict(data1)

# Creates dataframe with the labels
kmeans_df = pd.DataFrame(labels_km_1, columns=['cluster_id'])

# Create plot
fig = plt.figure(figsize=(5, 5))
kmeans_df.value_counts().sort_index().plot.bar()
plt.xlabel('Cluster', fontsize=13)
plt.title('K-Means clusters size distribution', fontsize=14)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','K 4'], rotation=0)
plt.show()

# Dimensionality reduction

from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, silhouette_samples
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans, DBSCAN, MeanShift, estimate_bandwidth, AgglomerativeClustering
from sklearn.mixture import GaussianMixture

# Initialize a new PCA model with a default number of components.
pca = PCA()
pca.fit(data1)


explained_variance = pca.explained_variance_

# Plot explained variance
fig = plt.figure(figsize=(7,7))
plt.plot(np.arange(1,10,1), explained_variance)
plt.xlabel('N of components', fontsize=13)
plt.ylabel('Explained variance', fontsize=13)
plt.xticks([0,5,10,15,20,40,60,80])
plt.show()

pca.explained_variance_ratio_

array([3.25651506e-01, 2.37407925e-01, 1.52562020e-01, 1.25313867e-01,
       8.01549678e-02, 5.01002681e-02, 2.77228757e-02, 1.05215468e-03,
       3.44155412e-05])

#Scree plot

import numpy as np
fig, ax = plt.subplots(figsize=(12, 8))

num_components = pca.n_components_
idx = np.arange(num_components)
vals = pca.explained_variance_ratio_*100

ax.plot(idx, vals, color='blue')
ax.scatter(idx, vals, color='blue', s=50)

for i in range(9):
    ax.annotate(r"{:2.2f}%".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))

ax.set_title('Scree Plot')
ax.set_xlabel('Principal Components')
ax.set_ylabel('% Variance Explained')

ax.set_xticklabels(idx+1, fontsize=12)
ax.set_xticks(np.arange(9))
ax.set_xlim(left=0-0.5, right=8+0.5)
ax.set_ylim(top=max(vals)+5)

(-1.624643895711861, 37.56515055083923)

pca = PCA().fit(data1)

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)

fig, ax = plt.subplots()
xi = np.arange(1, 10, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
print(y)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')

plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')

plt.axhline(y=0.99, color='r', linestyle='--')
plt.axvline(x=7, color='r', linestyle='--')
plt.text(0.5, 0.85, '99% cut-off threshold', color = 'red', fontsize=16)
plt.text(0.5, 0.70, 'n_components=7', color = 'black', fontsize=14)
ax.grid(axis='x')
plt.show()

[0.32565151 0.56305943 0.71562145 0.84093532 0.92109029 0.97119055
 0.99891343 0.99996558 1.        ]

pca = PCA(n_components=7)
x_7cols = pca.fit_transform(data1)

wcss = []

for k in range(1,10):
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(x_7cols)
    wcss.append(kmeans.inertia_)

wcss

[594871.9215496404,
 450064.81788699894,
 385397.46616398153,
 337230.4544508495,
 304819.9400492358,
 276731.34842181613,
 260563.21786454768,
 244855.48117241397,
 230492.67405957662]

x_7cols.shape

(63670, 7)

# Visualization of k values:
fig,ax = plt.subplots()
ax.plot(range(1,10), wcss, color='red',marker='x')
plt.axvline(x=4,color='blue',linestyle='--')
plt.title('Graph of k values and WCSS')
plt.xlabel('k values')
plt.ylabel('wcss values')
ax.annotate('elbow=4',xy=(4.2,450000),xytext=(4.5,450000),
                horizontalalignment='left',
                verticalalignment='top',size=16)

plt.show()

# Initializes the model with selected K, fit and predict the labels for the dataset
kmeans = KMeans(n_clusters=4, random_state=42).fit(x_7cols)
labels_km = kmeans.predict(x_7cols)

# Creates dataframe with the labels
kmeans_df = pd.DataFrame(labels_km, columns=['cluster_id'])

# Create plot
fig = plt.figure(figsize=(5, 5))
kmeans_df.value_counts().sort_index().plot.bar()
plt.xlabel('Cluster', fontsize=13)
plt.title('K-Means clusters size distribution', fontsize=14)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','K 4'], rotation=0)
plt.show()

unscaled_data=pd.read_csv('Unscaled_data.csv')

df = unscaled_data.copy(deep=True)
df['label'] = labels_km

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 63670 entries, 0 to 63669
Data columns (total 32 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Nationality           63670 non-null  object 
 1   Age                   63670 non-null  float64
 2   DaysSinceCreation     63670 non-null  float64
 3   AverageLeadTime       63670 non-null  int64  
 4   LodgingRevenue        63670 non-null  float64
 5   OtherRevenue          63670 non-null  float64
 6   BookingsCanceled      63670 non-null  float64
 7   BookingsNoShowed      63670 non-null  float64
 8   BookingsCheckedIn     63670 non-null  float64
 9   PersonsNights         63670 non-null  float64
 10  RoomNights            63670 non-null  float64
 11  DaysSinceLastStay     63670 non-null  float64
 12  DaysSinceFirstStay    63670 non-null  float64
 13  DistributionChannel   63670 non-null  object 
 14  MarketSegment         63670 non-null  object 
 15  SRHighFloor           63670 non-null  int64  
 16  SRLowFloor            63670 non-null  int64  
 17  SRAccessibleRoom      63670 non-null  int64  
 18  SRMediumFloor         63670 non-null  int64  
 19  SRBathtub             63670 non-null  int64  
 20  SRShower              63670 non-null  int64  
 21  SRCrib                63670 non-null  int64  
 22  SRKingSizeBed         63670 non-null  int64  
 23  SRTwinBed             63670 non-null  int64  
 24  SRNearElevator        63670 non-null  int64  
 25  SRAwayFromElevator    63670 non-null  int64  
 26  SRNoAlcoholInMiniBar  63670 non-null  int64  
 27  SRQuietRoom           63670 non-null  int64  
 28  month                 63670 non-null  int64  
 29  year                  63670 non-null  int64  
 30  Day                   63670 non-null  object 
 31  label                 63670 non-null  int32  
dtypes: float64(11), int32(1), int64(16), object(4)
memory usage: 15.3+ MB

unscaled_data['PersonsNights'].unique()

array([ 8., 10.,  4.,  6.,  3., 14.,  1.,  2., 12.,  9.,  5., 11., 13.,
        7.])

plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],palette="tab10")
plt.legend()
plt.show()

df.to_csv(r'D:\capstone project\hotel\Unscaled_data_Kmeans.csv', index = False, header=True)

plt_1=plt.figure(figsize=(15,55))
sns.relplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],
            col=df['PersonsNights'],palette="tab10",col_wrap=3)

plt.show()

<Figure size 1080x3960 with 0 Axes>

PERFORMANCE METRICS

from sklearn import metrics
from sklearn.metrics import davies_bouldin_score

#SILHOUETTE SCORE

metrics.silhouette_score(data1, labels_km, metric='euclidean')

0.22984897613849073

#Calinski-Harabasz Index

#BEFORE PCA

metrics.calinski_harabasz_score(data1, labels_km_1)

16182.805709398132

#AFTER PCA
metrics.calinski_harabasz_score(x_7cols, labels_km)

16213.43252396753

#Davies-Bouldin Index

#BEFORE PCA
davies_bouldin_score(data1, labels_km_1)

1.4089534977687062

#AFTER PCA
davies_bouldin_score(x_7cols, labels_km)

1.4101170804318928

CLASSIFICATION

DECISION TREE CLASSIFIER

df_k = data1.copy(deep=True)
df_k['label'] = labels_km

from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split

X = df_k.drop(['label'],axis=1)
y = df_k[['label']]
  
# dividing X, y into train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
  

from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier

tuned_paramaters = [{'criterion': ['entropy', 'gini'], 
                     'max_depth': range(2, 10),
                     'max_features': ["sqrt", "log2"],
                     'min_samples_split': range(2,10),
                     'min_samples_leaf': range(1,10),
                     'max_leaf_nodes': range(1, 10)}]
decision_tree_classification = DecisionTreeClassifier(random_state = 10)
tree_grid = GridSearchCV(estimator = decision_tree_classification, 
                         param_grid = tuned_paramaters, 
                         cv = 5)
tree_grid_model = tree_grid.fit(X_train, y_train)
print('Best parameters for decision tree classifier: ', tree_grid_model.best_params_, '\n')

Best parameters for decision tree classifier:  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'max_leaf_nodes': 8, 'min_samples_leaf': 1, 'min_samples_split': 2} 

# training a DescisionTreeClassifier

dtree_model = DecisionTreeClassifier(criterion= 'gini', max_depth= 4, max_features= 'sqrt', 
                                     max_leaf_nodes= 8, min_samples_leaf=1, min_samples_split= 2).fit(X_train, y_train)
dtree_predictions_test = dtree_model.predict(X_test)
dtree_predictions_train = dtree_model.predict(X_train)  
# creating a confusion matrix
cm = confusion_matrix(y_test, dtree_predictions_test)

score={}

#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,dtree_predictions_train))

              precision    recall  f1-score   support

           0       0.95      0.46      0.62      6011
           1       0.89      0.83      0.86      8892
           2       0.90      0.95      0.93     16220
           3       0.82      0.96      0.89     16629

    accuracy                           0.87     47752
   macro avg       0.89      0.80      0.82     47752
weighted avg       0.88      0.87      0.86     47752

x=round(accuracy_score(y_train,dtree_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,dtree_predictions_train))

Train accuracy 0.8709373429385157

#Test accuracy:
print(classification_report(y_test,dtree_predictions_test))

              precision    recall  f1-score   support

           0       0.95      0.45      0.61      2042
           1       0.87      0.82      0.85      2912
           2       0.89      0.95      0.92      5502
           3       0.82      0.96      0.88      5462

    accuracy                           0.86     15918
   macro avg       0.88      0.79      0.81     15918
weighted avg       0.87      0.86      0.85     15918

y=round(accuracy_score(y_train,dtree_predictions_train),2)
print('Test accuracy',accuracy_score(y_test,dtree_predictions_test))

Test accuracy 0.8638019851740169

score.update({'DecisionTree':[x,y]})

NAIVE BAYES CLASSIFIER

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB().fit(X_train, y_train)
gnb_predictions_train = gnb.predict(X_train)
gnb_predictions_test = gnb.predict(X_test)

#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,gnb_predictions_train))

              precision    recall  f1-score   support

           0       0.91      0.93      0.92      6011
           1       0.88      0.95      0.91      8892
           2       0.97      0.96      0.96     16220
           3       0.97      0.93      0.95     16629

    accuracy                           0.94     47752
   macro avg       0.93      0.94      0.94     47752
weighted avg       0.94      0.94      0.94     47752

x1=round(accuracy_score(y_train,gnb_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,gnb_predictions_train))

Train accuracy 0.9433740995141565

#Test accuracy:
print(classification_report(y_test,gnb_predictions_test))

              precision    recall  f1-score   support

           0       0.92      0.93      0.93      2042
           1       0.87      0.96      0.91      2912
           2       0.97      0.96      0.96      5502
           3       0.98      0.93      0.95      5462

    accuracy                           0.94     15918
   macro avg       0.93      0.94      0.94     15918
weighted avg       0.95      0.94      0.95     15918

y1=round(accuracy_score(y_test,gnb_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,gnb_predictions_test))

Test accuracy 0.9448423168739791

score.update({'NaiveBayes':[x1,y1]})

KNN CLASSIFIER

from sklearn.neighbors import KNeighborsClassifier

tuned_paramaters = {'n_neighbors': np.arange(1, 25, 2),
                   'metric': ['hamming','euclidean','manhattan','Chebyshev']}
knn_classification = KNeighborsClassifier()
knn_grid = GridSearchCV(estimator = knn_classification, 
                        param_grid = tuned_paramaters, 
                        cv = 5, 
                        scoring = 'accuracy')
knn_grid.fit(X_train, y_train)
print('Best parameters for KNN Classifier: ', knn_grid.best_params_, '\n')

Best parameters for KNN Classifier:  {'metric': 'manhattan', 'n_neighbors': 7} 

knn = KNeighborsClassifier(n_neighbors = 7,metric='manhattan').fit(X_train, y_train)
knn_predictions_train = knn.predict(X_train)
knn_predictions_test = knn.predict(X_test)

#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,knn_predictions_train))

              precision    recall  f1-score   support

           0       0.98      0.97      0.97      6011
           1       0.98      0.99      0.98      8892
           2       0.99      0.98      0.99     16220
           3       0.99      0.99      0.99     16629

    accuracy                           0.99     47752
   macro avg       0.98      0.98      0.98     47752
weighted avg       0.99      0.99      0.99     47752

x2=round(accuracy_score(y_train,knn_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,knn_predictions_train))

Train accuracy 0.9850686882224828

#Test accuracy:
print(classification_report(y_test,knn_predictions_test))

              precision    recall  f1-score   support

           0       0.97      0.95      0.96      2042
           1       0.96      0.98      0.97      2912
           2       0.98      0.97      0.98      5502
           3       0.98      0.98      0.98      5462

    accuracy                           0.98     15918
   macro avg       0.97      0.97      0.97     15918
weighted avg       0.98      0.98      0.98     15918

y2=round(accuracy_score(y_test,knn_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,knn_predictions_test))

Test accuracy 0.9753737906772207

score.update({'KNN':[x2,y2]})

SVM CLASSIFIER

from sklearn.svm import SVC

param_grid = {'C': [0.1, 1, 10, 100, 1000], 
              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
              'kernel': ['rbf']} 
  
grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)
grid.fit(X_train, y_train)

Fitting 5 folds for each of 25 candidates, totalling 125 fits
[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.974 total time=  47.1s
[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.975 total time=  41.0s
[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.976 total time=  39.7s
[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.978 total time=  42.1s
[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.974 total time=  43.2s
[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.982 total time=  14.8s
[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.982 total time=  15.0s
[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.980 total time=  14.8s
[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.983 total time=  15.2s
[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.981 total time=  14.9s
[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.973 total time=  26.4s
[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.971 total time=  26.2s
[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.972 total time=  27.0s
[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.973 total time=  27.2s
[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.972 total time=  27.1s
[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.948 total time=  58.9s
[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.945 total time= 1.0min
[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.947 total time=  58.5s
[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.945 total time=  57.8s
[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.949 total time=  57.1s
[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.767 total time= 1.8min
[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.763 total time= 1.8min
[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.766 total time= 1.8min
[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.768 total time= 1.9min
[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.761 total time= 1.8min
[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.983 total time=  28.7s
[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.984 total time=  27.3s
[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.985 total time=  26.0s
[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.986 total time=  25.6s
[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.985 total time=  25.7s
[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.990 total time=   6.9s
[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.990 total time=   6.8s
[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.991 total time=   6.8s
[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.992 total time=   6.8s
[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.990 total time=   7.0s
[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time=  11.8s
[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time=  11.9s
[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time=  11.8s
[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.990 total time=  11.9s
[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.989 total time=  12.5s
[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.974 total time=  25.8s
[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.972 total time=  27.4s
[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.973 total time=  27.6s
[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.975 total time=  30.9s
[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.974 total time=  27.6s
[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.948 total time= 1.0min
[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.945 total time= 1.0min
[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.947 total time=  59.2s
[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.946 total time=  58.9s
[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.949 total time= 1.0min
[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.986 total time=  25.2s
[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.984 total time=  24.4s
[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.984 total time=  24.0s
[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.985 total time=  25.2s
[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.987 total time=  23.2s
[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.993 total time=   3.8s
[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.994 total time=   3.9s
[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.992 total time=   4.3s
[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.994 total time=   4.4s
[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.995 total time=   4.4s
[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.994 total time=   5.9s
[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.993 total time=   6.1s
[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.993 total time=   6.5s
[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.995 total time=   6.7s
[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.995 total time=   6.6s
[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.990 total time=  12.8s
[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.988 total time=  13.3s
[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.989 total time=  14.0s
[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.991 total time=  13.6s
[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.990 total time=  12.9s
[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.974 total time=  27.8s
[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.972 total time=  26.9s
[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.973 total time=  26.8s
[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.975 total time=  26.8s
[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.974 total time=  28.6s
[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.985 total time=  27.7s
[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.983 total time=  26.5s
[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.985 total time=  26.2s
[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.986 total time=  24.5s
[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.985 total time=  22.3s
[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.994 total time=   2.3s
[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.995 total time=   2.6s
[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.993 total time=   2.3s
[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.994 total time=   2.4s
[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.996 total time=   2.3s
[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.996 total time=   3.1s
[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.997 total time=   2.9s
[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.996 total time=   3.2s
[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.996 total time=   3.0s
[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.997 total time=   3.0s
[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.995 total time=   6.0s
[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.995 total time=   5.9s
[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.994 total time=   5.6s
[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.996 total time=   5.8s
[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.996 total time=   5.7s
[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.990 total time=  11.7s
[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.988 total time=  11.5s
[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.989 total time=  11.6s
[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.991 total time=  11.7s
[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.990 total time=  12.0s
[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.985 total time=  21.7s
[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.983 total time=  21.8s
[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.984 total time=  24.3s
[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.986 total time=  22.0s
[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.984 total time=  22.2s
[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.994 total time=   2.3s
[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.994 total time=   2.3s
[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.994 total time=   2.0s
[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.995 total time=   2.0s
[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.995 total time=   2.0s
[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.997 total time=   2.0s
[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.997 total time=   2.2s
[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.996 total time=   1.9s
[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.997 total time=   1.9s
[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.998 total time=   1.9s
[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.997 total time=   3.0s
[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.997 total time=   3.1s
[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.998 total time=   3.1s
[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.998 total time=   3.1s
[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.998 total time=   3.2s
[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.995 total time=   6.3s
[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.994 total time=   6.2s
[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.994 total time=   6.0s
[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.995 total time=   6.0s
[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.995 total time=   6.1s

GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100, 1000],
                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
                         'kernel': ['rbf']},
             verbose=3)

print(grid.best_params_)
print(grid.best_estimator_)

{'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}
SVC(C=1000, gamma=0.001)

svc = SVC(C= 1000, gamma= 0.001,kernel= 'rbf')
svc.fit(X_train, y_train)
SVC_predictions_train = svc.predict(X_train)
SVC_predictions_test = svc.predict(X_test)

#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,SVC_predictions_train))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00      6011
           1       1.00      1.00      1.00      8892
           2       1.00      1.00      1.00     16220
           3       1.00      1.00      1.00     16629

    accuracy                           1.00     47752
   macro avg       1.00      1.00      1.00     47752
weighted avg       1.00      1.00      1.00     47752

x3=round(accuracy_score(y_train,SVC_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,SVC_predictions_train))

Train accuracy 0.9985340928128664

#Test accuracy:
print(classification_report(y_test,SVC_predictions_test))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00      2042
           1       1.00      1.00      1.00      2912
           2       1.00      1.00      1.00      5502
           3       1.00      1.00      1.00      5462

    accuracy                           1.00     15918
   macro avg       1.00      1.00      1.00     15918
weighted avg       1.00      1.00      1.00     15918

y3=round(accuracy_score(y_test,SVC_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,SVC_predictions_test))

Test accuracy 0.9979896971981405

score.update({'SVC':[x3,y3]})

ENSEMBLE

XG BOOST CLASSIFIER

import xgboost
from xgboost import XGBClassifier

tuning_parameters = {'learning_rate': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
                     'max_depth': range(3,10),
                     'gamma': [0, 1, 2, 3, 4]}
xgb_model = XGBClassifier()
xgb_grid = GridSearchCV(estimator = xgb_model, param_grid = tuning_parameters, cv = 3)
xgb_grid.fit(X_train, y_train)
print('Best parameters for XGBoost classifier: ', xgb_grid.best_params_, '\n')

[15:30:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:31:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:32:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:33:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:34:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:35:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:36:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:37:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:38:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:39:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:40:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:41:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:42:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:43:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:44:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:45:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:46:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:47:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:48:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:49:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:50:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:51:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:52:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:53:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:54:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:55:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:56:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:57:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:58:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[15:59:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:00:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:01:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:02:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:03:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:04:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:05:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:06:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:07:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:08:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:09:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:10:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:11:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:12:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:13:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:14:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:15:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:16:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:17:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:18:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:19:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:20:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:21:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:22:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:23:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:24:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:25:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:26:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:27:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:28:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:29:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:30:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:31:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:32:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:33:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:34:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:35:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:36:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:36:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[16:36:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Best parameters for XGBoost classifier:  {'gamma': 0, 'learning_rate': 0.5, 'max_depth': 3} 

print('Best parameters for XGBoost classifier: ', xgb_grid.best_params_, '\n')

Best parameters for XGBoost classifier:  {'gamma': 0, 'learning_rate': 0.5, 'max_depth': 3} 

xgb_model = XGBClassifier(max_depth = 3, gamma = 0,learning_rate=0.5)

# fit the model using fit() on train data
xgb_model.fit(X_train, y_train)
xgb_predictions_test = xgb_model.predict(X_test)
xgb_predictions_train = xgb_model.predict(X_train)

[16:51:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

#Accuracy:
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
#Train accuracy:
print(classification_report(y_train,xgb_predictions_train))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00      6011
           1       1.00      1.00      1.00      8892
           2       1.00      1.00      1.00     16220
           3       1.00      1.00      1.00     16629

    accuracy                           1.00     47752
   macro avg       1.00      1.00      1.00     47752
weighted avg       1.00      1.00      1.00     47752

x4=round(accuracy_score(y_train,xgb_predictions_train),2)
print('Train accuracy',accuracy_score(y_train,xgb_predictions_train))

Train accuracy 0.9978220807505445

#Test accuracy:
print(classification_report(y_test,xgb_predictions_test))

              precision    recall  f1-score   support

           0       0.99      0.98      0.98      2042
           1       0.99      0.99      0.99      2912
           2       0.99      0.99      0.99      5502
           3       0.99      0.99      0.99      5462

    accuracy                           0.99     15918
   macro avg       0.99      0.99      0.99     15918
weighted avg       0.99      0.99      0.99     15918

y4=round(accuracy_score(y_test,xgb_predictions_test),2)
print('Test accuracy',accuracy_score(y_test,xgb_predictions_test))

Test accuracy 0.9905138836537254

score.update({'XGB':[x4,y4]})

score=pd.DataFrame(score).T

score

	0	1
DecisionTree	0.87	0.87
NaiveBayes	0.94	0.94
KNN	0.99	0.98
SVC	1.00	1.00
XGB	1.00	0.99

sns.lineplot(score.index,score[0],color='blue',label='Train_Score')
sns.lineplot(score.index,score[1],color='red',label='Test_Score',linestyle='--')
plt.xlabel('Classification Model')
plt.ylabel('Accuracy_Score')
plt.title('Accuracy score for Classifiers')
plt.legend()
plt.show()

K-PROTOTYPE

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from matplotlib import style

from kmodes.kmodes import KModes
from kmodes.kprototypes import KPrototypes

from tqdm import tqdm
from dython.model_utils import roc_graph
from dython.nominal import associations

import warnings
warnings.filterwarnings("ignore")

df = pd.read_csv('C:/Users/bannu/Downloads/GL_Project/New folder/processed_file.csv')

df.head()

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	PersonsNights	RoomNights	DaysSinceLastStay	DaysSinceFirstStay	DistributionChannel	MarketSegment	Continent_Name
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	0.591825	1.389564	-1.261711	1.578742	Corporate	Corporate	Europe
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	1.154189	1.389564	1.584588	1.578742	Travel Agent/Operator	Travel Agent/Operator	Europe
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	1.154189	1.389564	1.584588	1.578742	Travel Agent/Operator	Travel Agent/Operator	Europe
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	-0.532903	-0.675838	1.584588	1.578742	Travel Agent/Operator	Other	Asia
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	1.154189	1.389564	1.584588	1.578742	Travel Agent/Operator	Other	Europe

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 63670 entries, 0 to 63669
Data columns (total 12 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Age                  63670 non-null  float64
 1   DaysSinceCreation    63670 non-null  float64
 2   AverageLeadTime      63670 non-null  float64
 3   LodgingRevenue       63670 non-null  float64
 4   OtherRevenue         63670 non-null  float64
 5   PersonsNights        63670 non-null  float64
 6   RoomNights           63670 non-null  float64
 7   DaysSinceLastStay    63670 non-null  float64
 8   DaysSinceFirstStay   63670 non-null  float64
 9   DistributionChannel  63670 non-null  object 
 10  MarketSegment        63670 non-null  object 
 11  Continent_Name       63670 non-null  object 
dtypes: float64(9), object(3)
memory usage: 5.8+ MB

cost = []
for cluster in tqdm(range(1, 20)):
    try:
        kprototype = KPrototypes(n_jobs = -1, n_clusters = cluster, init = 'cao', n_init=15,max_iter =30)
        kprototype.fit_predict(df, categorical = [9,10,11])
        cost.append(kprototype.cost_)
        print('Cluster initiation: {}'.format(cluster)),
    except:
        break

Converting the results and plotting them

df_cost = pd.DataFrame({'Cluster':range(1, 20), 'Cost':cost})

# Data viz
plotnine.options.figure_size = (8, 4.8)
(
    ggplot(data = df_cost)+
    geom_line(aes(x = 'Cluster',
                  y = 'Cost'))+
    geom_point(aes(x = 'Cluster',
                   y = 'Cost'))+
    geom_label(aes(x = 'Cluster',
                   y = 'Cost',
                   label = 'Cluster'),
               size = 10,
               nudge_y = 1000) +
    labs(title = 'Optimal number of cluster with Elbow Method')+
    xlab('Number of Clusters k')+
    ylab('Cost')+
    theme_minimal()
)

<ggplot: (8784480325036)>

From above Graph we get that the optimal value of K is 4

k_proto = KPrototypes(n_clusters= 4 , verbose= 2, max_iter = 50 , init= 'huang',n_init=15)
clusters = k_proto.fit_predict(df, categorical = [9,10,11])

Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 1, iteration: 1/50, moves: 16370, ncost: 332182.0527192895
Run: 1, iteration: 2/50, moves: 3371, ncost: 331071.4797125874
Run: 1, iteration: 3/50, moves: 1077, ncost: 330913.1336543894
Run: 1, iteration: 4/50, moves: 522, ncost: 330879.3844547407
Run: 1, iteration: 5/50, moves: 222, ncost: 330872.51808695274
Run: 1, iteration: 6/50, moves: 89, ncost: 330871.1669122444
Run: 1, iteration: 7/50, moves: 39, ncost: 330870.78217283415
Run: 1, iteration: 8/50, moves: 30, ncost: 330870.6229282999
Run: 1, iteration: 9/50, moves: 25, ncost: 330870.51364001964
Run: 1, iteration: 10/50, moves: 4, ncost: 330870.50747790706
Run: 1, iteration: 11/50, moves: 2, ncost: 330870.5052187324
Run: 1, iteration: 12/50, moves: 1, ncost: 330870.49822402355
Run: 1, iteration: 13/50, moves: 4, ncost: 330870.47013030096
Run: 1, iteration: 14/50, moves: 15, ncost: 330870.3954062396
Run: 1, iteration: 15/50, moves: 1, ncost: 330870.39465264813
Run: 1, iteration: 16/50, moves: 0, ncost: 330870.39465264813
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 2, iteration: 1/50, moves: 15814, ncost: 331064.94847601646
Run: 2, iteration: 2/50, moves: 4081, ncost: 329427.89422228246
Run: 2, iteration: 3/50, moves: 1906, ncost: 328931.09776377183
Run: 2, iteration: 4/50, moves: 1097, ncost: 328741.65114418307
Run: 2, iteration: 5/50, moves: 777, ncost: 328623.9628505583
Run: 2, iteration: 6/50, moves: 720, ncost: 328477.105754203
Run: 2, iteration: 7/50, moves: 729, ncost: 328338.8798468174
Run: 2, iteration: 8/50, moves: 610, ncost: 328235.66929272364
Run: 2, iteration: 9/50, moves: 603, ncost: 328124.39471500536
Run: 2, iteration: 10/50, moves: 662, ncost: 328010.3773620553
Run: 2, iteration: 11/50, moves: 431, ncost: 327974.93504220195
Run: 2, iteration: 12/50, moves: 255, ncost: 327961.9372389368
Run: 2, iteration: 13/50, moves: 114, ncost: 327958.80574702984
Run: 2, iteration: 14/50, moves: 112, ncost: 327954.72523929225
Run: 2, iteration: 15/50, moves: 84, ncost: 327952.8984636599
Run: 2, iteration: 16/50, moves: 40, ncost: 327952.58131710236
Run: 2, iteration: 17/50, moves: 19, ncost: 327952.4941192523
Run: 2, iteration: 18/50, moves: 16, ncost: 327952.4168781412
Run: 2, iteration: 19/50, moves: 19, ncost: 327952.3520741611
Run: 2, iteration: 20/50, moves: 9, ncost: 327952.3296066011
Run: 2, iteration: 21/50, moves: 1, ncost: 327952.3292628051
Run: 2, iteration: 22/50, moves: 0, ncost: 327952.3292628051
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 3, iteration: 1/50, moves: 29377, ncost: 346089.2256848074
Run: 3, iteration: 2/50, moves: 10036, ncost: 333959.5950330606
Run: 3, iteration: 3/50, moves: 2128, ncost: 333500.1599934982
Run: 3, iteration: 4/50, moves: 1026, ncost: 333389.5348186114
Run: 3, iteration: 5/50, moves: 541, ncost: 333357.6991360698
Run: 3, iteration: 6/50, moves: 286, ncost: 333348.1791672376
Run: 3, iteration: 7/50, moves: 151, ncost: 333345.1417213754
Run: 3, iteration: 8/50, moves: 110, ncost: 333343.01122948
Run: 3, iteration: 9/50, moves: 93, ncost: 333338.0483779666
Run: 3, iteration: 10/50, moves: 231, ncost: 333321.2509086866
Run: 3, iteration: 11/50, moves: 254, ncost: 333300.1169902591
Run: 3, iteration: 12/50, moves: 299, ncost: 333273.5507613123
Run: 3, iteration: 13/50, moves: 336, ncost: 333234.8901824862
Run: 3, iteration: 14/50, moves: 421, ncost: 333176.76189495827
Run: 3, iteration: 15/50, moves: 627, ncost: 333057.14972587
Run: 3, iteration: 16/50, moves: 638, ncost: 332927.2989999464
Run: 3, iteration: 17/50, moves: 680, ncost: 332756.91911392036
Run: 3, iteration: 18/50, moves: 778, ncost: 332544.50876860146
Run: 3, iteration: 19/50, moves: 831, ncost: 332276.9385628156
Run: 3, iteration: 20/50, moves: 881, ncost: 332000.02574535285
Run: 3, iteration: 21/50, moves: 820, ncost: 331723.068837588
Run: 3, iteration: 22/50, moves: 937, ncost: 331393.2679034874
Run: 3, iteration: 23/50, moves: 767, ncost: 331180.13661276794
Run: 3, iteration: 24/50, moves: 614, ncost: 331027.44595540885
Run: 3, iteration: 25/50, moves: 509, ncost: 330935.5843401023
Run: 3, iteration: 26/50, moves: 372, ncost: 330898.1448469224
Run: 3, iteration: 27/50, moves: 265, ncost: 330881.0053101479
Run: 3, iteration: 28/50, moves: 133, ncost: 330876.52173072007
Run: 3, iteration: 29/50, moves: 79, ncost: 330873.67414245754
Run: 3, iteration: 30/50, moves: 74, ncost: 330871.30987174256
Run: 3, iteration: 31/50, moves: 39, ncost: 330870.7828011773
Run: 3, iteration: 32/50, moves: 23, ncost: 330870.55462097184
Run: 3, iteration: 33/50, moves: 13, ncost: 330870.47374855785
Run: 3, iteration: 34/50, moves: 9, ncost: 330870.437255312
Run: 3, iteration: 35/50, moves: 2, ncost: 330870.43449498026
Run: 3, iteration: 36/50, moves: 2, ncost: 330870.43215123384
Run: 3, iteration: 37/50, moves: 3, ncost: 330870.42450064543
Run: 3, iteration: 38/50, moves: 5, ncost: 330870.4095156377
Run: 3, iteration: 39/50, moves: 3, ncost: 330870.40161713306
Run: 3, iteration: 40/50, moves: 1, ncost: 330870.40010763047
Run: 3, iteration: 41/50, moves: 0, ncost: 330870.40010763047
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 4, iteration: 1/50, moves: 18893, ncost: 339561.7348435977
Run: 4, iteration: 2/50, moves: 6722, ncost: 331918.1693104725
Run: 4, iteration: 3/50, moves: 4032, ncost: 329294.31514153234
Run: 4, iteration: 4/50, moves: 2148, ncost: 328610.0485875048
Run: 4, iteration: 5/50, moves: 1426, ncost: 328262.1404197179
Run: 4, iteration: 6/50, moves: 924, ncost: 328119.9148017382
Run: 4, iteration: 7/50, moves: 653, ncost: 328045.76471554174
Run: 4, iteration: 8/50, moves: 556, ncost: 327990.67354892363
Run: 4, iteration: 9/50, moves: 438, ncost: 327961.6268966945
Run: 4, iteration: 10/50, moves: 215, ncost: 327955.289753461
Run: 4, iteration: 11/50, moves: 103, ncost: 327953.88643309264
Run: 4, iteration: 12/50, moves: 64, ncost: 327953.3087166805
Run: 4, iteration: 13/50, moves: 45, ncost: 327952.9713238563
Run: 4, iteration: 14/50, moves: 29, ncost: 327952.80561984546
Run: 4, iteration: 15/50, moves: 25, ncost: 327952.63398581825
Run: 4, iteration: 16/50, moves: 30, ncost: 327952.4108709016
Run: 4, iteration: 17/50, moves: 14, ncost: 327952.3693699302
Run: 4, iteration: 18/50, moves: 6, ncost: 327952.35642089375
Run: 4, iteration: 19/50, moves: 2, ncost: 327952.349775432
Run: 4, iteration: 20/50, moves: 1, ncost: 327952.34722393064
Run: 4, iteration: 21/50, moves: 0, ncost: 327952.34722393064
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 5, iteration: 1/50, moves: 16862, ncost: 332029.7642745474
Run: 5, iteration: 2/50, moves: 4388, ncost: 330103.97621850815
Run: 5, iteration: 3/50, moves: 2448, ncost: 329379.907129429
Run: 5, iteration: 4/50, moves: 1702, ncost: 328971.6340607473
Run: 5, iteration: 5/50, moves: 1309, ncost: 328712.31935572496
Run: 5, iteration: 6/50, moves: 1071, ncost: 328520.70991536166
Run: 5, iteration: 7/50, moves: 871, ncost: 328350.2649682622
Run: 5, iteration: 8/50, moves: 782, ncost: 328222.9740850017
Run: 5, iteration: 9/50, moves: 666, ncost: 328135.96260721196
Run: 5, iteration: 10/50, moves: 565, ncost: 328072.7366671492
Run: 5, iteration: 11/50, moves: 501, ncost: 328019.2398626853
Run: 5, iteration: 12/50, moves: 429, ncost: 327985.4892262595
Run: 5, iteration: 13/50, moves: 348, ncost: 327966.87230989593
Run: 5, iteration: 14/50, moves: 201, ncost: 327960.01242790703
Run: 5, iteration: 15/50, moves: 158, ncost: 327955.41377822496
Run: 5, iteration: 16/50, moves: 100, ncost: 327953.9809322712
Run: 5, iteration: 17/50, moves: 62, ncost: 327953.3758901359
Run: 5, iteration: 18/50, moves: 46, ncost: 327953.0387118296
Run: 5, iteration: 19/50, moves: 30, ncost: 327952.8397272576
Run: 5, iteration: 20/50, moves: 19, ncost: 327952.7253174698
Run: 5, iteration: 21/50, moves: 25, ncost: 327952.5268988979
Run: 5, iteration: 22/50, moves: 24, ncost: 327952.387522905
Run: 5, iteration: 23/50, moves: 8, ncost: 327952.3693699297
Run: 5, iteration: 24/50, moves: 6, ncost: 327952.35642089316
Run: 5, iteration: 25/50, moves: 2, ncost: 327952.3497754313
Run: 5, iteration: 26/50, moves: 1, ncost: 327952.3472239298
Run: 5, iteration: 27/50, moves: 0, ncost: 327952.3472239298
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 6, iteration: 1/50, moves: 19984, ncost: 344915.3462015841
Run: 6, iteration: 2/50, moves: 8609, ncost: 333744.3495210195
Run: 6, iteration: 3/50, moves: 5675, ncost: 329893.3370119469
Run: 6, iteration: 4/50, moves: 2997, ncost: 328817.75813484774
Run: 6, iteration: 5/50, moves: 1343, ncost: 328542.8811673044
Run: 6, iteration: 6/50, moves: 847, ncost: 328408.26198056637
Run: 6, iteration: 7/50, moves: 689, ncost: 328309.00151370664
Run: 6, iteration: 8/50, moves: 689, ncost: 328173.0991784212
Run: 6, iteration: 9/50, moves: 673, ncost: 328050.8414886471
Run: 6, iteration: 10/50, moves: 590, ncost: 327981.7494027284
Run: 6, iteration: 11/50, moves: 342, ncost: 327963.6652303224
Run: 6, iteration: 12/50, moves: 161, ncost: 327959.06233444466
Run: 6, iteration: 13/50, moves: 117, ncost: 327954.86332033377
Run: 6, iteration: 14/50, moves: 93, ncost: 327952.8874969845
Run: 6, iteration: 15/50, moves: 41, ncost: 327952.5726867776
Run: 6, iteration: 16/50, moves: 16, ncost: 327952.49626272
Run: 6, iteration: 17/50, moves: 20, ncost: 327952.4008624499
Run: 6, iteration: 18/50, moves: 14, ncost: 327952.3518192695
Run: 6, iteration: 19/50, moves: 9, ncost: 327952.3296066009
Run: 6, iteration: 20/50, moves: 1, ncost: 327952.3292628055
Run: 6, iteration: 21/50, moves: 0, ncost: 327952.3292628055
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 7, iteration: 1/50, moves: 28735, ncost: 342971.77590722975
Run: 7, iteration: 2/50, moves: 6647, ncost: 334240.58415789483
Run: 7, iteration: 3/50, moves: 3810, ncost: 330992.20057965384
Run: 7, iteration: 4/50, moves: 2653, ncost: 329682.2964339171
Run: 7, iteration: 5/50, moves: 1911, ncost: 329130.45079138706
Run: 7, iteration: 6/50, moves: 1475, ncost: 328808.50976640085
Run: 7, iteration: 7/50, moves: 1131, ncost: 328603.18836461176
Run: 7, iteration: 8/50, moves: 928, ncost: 328428.64055978134
Run: 7, iteration: 9/50, moves: 826, ncost: 328276.49032562145
Run: 7, iteration: 10/50, moves: 720, ncost: 328172.22866986913
Run: 7, iteration: 11/50, moves: 621, ncost: 328099.95636101894
Run: 7, iteration: 12/50, moves: 590, ncost: 328034.5059732224
Run: 7, iteration: 13/50, moves: 476, ncost: 327994.01820132526
Run: 7, iteration: 14/50, moves: 388, ncost: 327969.71621679846
Run: 7, iteration: 15/50, moves: 239, ncost: 327961.0991394548
Run: 7, iteration: 16/50, moves: 161, ncost: 327955.952526057
Run: 7, iteration: 17/50, moves: 99, ncost: 327954.3280972795
Run: 7, iteration: 18/50, moves: 75, ncost: 327953.5001150774
Run: 7, iteration: 19/50, moves: 49, ncost: 327953.1251397196
Run: 7, iteration: 20/50, moves: 35, ncost: 327952.892054883
Run: 7, iteration: 21/50, moves: 24, ncost: 327952.7514403655
Run: 7, iteration: 22/50, moves: 27, ncost: 327952.5433267152
Run: 7, iteration: 23/50, moves: 27, ncost: 327952.3875229048
Run: 7, iteration: 24/50, moves: 8, ncost: 327952.3693699304
Run: 7, iteration: 25/50, moves: 6, ncost: 327952.35642089247
Run: 7, iteration: 26/50, moves: 2, ncost: 327952.3497754312
Run: 7, iteration: 27/50, moves: 1, ncost: 327952.3472239296
Run: 7, iteration: 28/50, moves: 0, ncost: 327952.3472239296
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 8, iteration: 1/50, moves: 30097, ncost: 337828.496707534
Run: 8, iteration: 2/50, moves: 5818, ncost: 332942.32566174114
Run: 8, iteration: 3/50, moves: 3004, ncost: 331477.82941394165
Run: 8, iteration: 4/50, moves: 1610, ncost: 331033.6603364823
Run: 8, iteration: 5/50, moves: 826, ncost: 330920.476950288
Run: 8, iteration: 6/50, moves: 428, ncost: 330886.97299437446
Run: 8, iteration: 7/50, moves: 252, ncost: 330875.9173645625
Run: 8, iteration: 8/50, moves: 146, ncost: 330871.6425072911
Run: 8, iteration: 9/50, moves: 59, ncost: 330870.92695826385
Run: 8, iteration: 10/50, moves: 33, ncost: 330870.65084336186
Run: 8, iteration: 11/50, moves: 17, ncost: 330870.5577676345
Run: 8, iteration: 12/50, moves: 12, ncost: 330870.51754644816
Run: 8, iteration: 13/50, moves: 11, ncost: 330870.4652009683
Run: 8, iteration: 14/50, moves: 7, ncost: 330870.4287670057
Run: 8, iteration: 15/50, moves: 6, ncost: 330870.4025289204
Run: 8, iteration: 16/50, moves: 1, ncost: 330870.4007236011
Run: 8, iteration: 17/50, moves: 0, ncost: 330870.4007236011
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 9, iteration: 1/50, moves: 32567, ncost: 342557.4139099887
Run: 9, iteration: 2/50, moves: 6081, ncost: 337925.28796494176
Run: 9, iteration: 3/50, moves: 4068, ncost: 334951.3046702748
Run: 9, iteration: 4/50, moves: 2905, ncost: 333622.6110086613
Run: 9, iteration: 5/50, moves: 1524, ncost: 333306.5998919689
Run: 9, iteration: 6/50, moves: 839, ncost: 333195.82556226244
Run: 9, iteration: 7/50, moves: 709, ncost: 333072.69187604677
Run: 9, iteration: 8/50, moves: 663, ncost: 332942.76158289105
Run: 9, iteration: 9/50, moves: 685, ncost: 332774.8004421763
Run: 9, iteration: 10/50, moves: 781, ncost: 332566.13232252805
Run: 9, iteration: 11/50, moves: 831, ncost: 332300.1087750088
Run: 9, iteration: 12/50, moves: 886, ncost: 332016.8133406674
Run: 9, iteration: 13/50, moves: 829, ncost: 331739.95998503023
Run: 9, iteration: 14/50, moves: 931, ncost: 331410.7072878081
Run: 9, iteration: 15/50, moves: 780, ncost: 331192.58094839985
Run: 9, iteration: 16/50, moves: 642, ncost: 331031.11429016455
Run: 9, iteration: 17/50, moves: 522, ncost: 330936.21911703446
Run: 9, iteration: 18/50, moves: 371, ncost: 330898.4222693686
Run: 9, iteration: 19/50, moves: 268, ncost: 330881.1395190745
Run: 9, iteration: 20/50, moves: 137, ncost: 330876.5516950981
Run: 9, iteration: 21/50, moves: 83, ncost: 330873.67414245795
Run: 9, iteration: 22/50, moves: 74, ncost: 330871.30987174227
Run: 9, iteration: 23/50, moves: 39, ncost: 330870.7828011774
Run: 9, iteration: 24/50, moves: 23, ncost: 330870.5546209703
Run: 9, iteration: 25/50, moves: 13, ncost: 330870.47374855727
Run: 9, iteration: 26/50, moves: 9, ncost: 330870.43725531205
Run: 9, iteration: 27/50, moves: 2, ncost: 330870.43449498015
Run: 9, iteration: 28/50, moves: 2, ncost: 330870.43215123453
Run: 9, iteration: 29/50, moves: 3, ncost: 330870.4245006456
Run: 9, iteration: 30/50, moves: 5, ncost: 330870.40951563756
Run: 9, iteration: 31/50, moves: 3, ncost: 330870.40161713294
Run: 9, iteration: 32/50, moves: 1, ncost: 330870.4001076307
Run: 9, iteration: 33/50, moves: 0, ncost: 330870.4001076307
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 10, iteration: 1/50, moves: 13817, ncost: 343838.4401145477
Run: 10, iteration: 2/50, moves: 7561, ncost: 334372.1888281921
Run: 10, iteration: 3/50, moves: 4607, ncost: 331898.8560813422
Run: 10, iteration: 4/50, moves: 2107, ncost: 331300.6710739151
Run: 10, iteration: 5/50, moves: 1226, ncost: 330932.0323791534
Run: 10, iteration: 6/50, moves: 1068, ncost: 330517.05696105247
Run: 10, iteration: 7/50, moves: 1197, ncost: 330114.5373901234
Run: 10, iteration: 8/50, moves: 1170, ncost: 329839.1889918673
Run: 10, iteration: 9/50, moves: 971, ncost: 329670.20317120117
Run: 10, iteration: 10/50, moves: 753, ncost: 329565.3017385462
Run: 10, iteration: 11/50, moves: 579, ncost: 329510.0091609578
Run: 10, iteration: 12/50, moves: 405, ncost: 329486.80281079723
Run: 10, iteration: 13/50, moves: 268, ncost: 329475.07968835154
Run: 10, iteration: 14/50, moves: 264, ncost: 329462.2927811659
Run: 10, iteration: 15/50, moves: 359, ncost: 329443.9355907085
Run: 10, iteration: 16/50, moves: 286, ncost: 329432.6193410474
Run: 10, iteration: 17/50, moves: 229, ncost: 329425.09594674216
Run: 10, iteration: 18/50, moves: 158, ncost: 329421.0964490192
Run: 10, iteration: 19/50, moves: 120, ncost: 329418.48573775013
Run: 10, iteration: 20/50, moves: 94, ncost: 329417.131912286
Run: 10, iteration: 21/50, moves: 52, ncost: 329416.74004029075
Run: 10, iteration: 22/50, moves: 45, ncost: 329416.26802756696
Run: 10, iteration: 23/50, moves: 59, ncost: 329415.53249781934
Run: 10, iteration: 24/50, moves: 59, ncost: 329414.90654766967
Run: 10, iteration: 25/50, moves: 67, ncost: 329414.047668261
Run: 10, iteration: 26/50, moves: 66, ncost: 329413.2654348498
Run: 10, iteration: 27/50, moves: 57, ncost: 329412.7101748951
Run: 10, iteration: 28/50, moves: 94, ncost: 329410.6112939277
Run: 10, iteration: 29/50, moves: 117, ncost: 329408.463534639
Run: 10, iteration: 30/50, moves: 73, ncost: 329407.61453865515
Run: 10, iteration: 31/50, moves: 45, ncost: 329407.22322276665
Run: 10, iteration: 32/50, moves: 32, ncost: 329407.0334538882
Run: 10, iteration: 33/50, moves: 40, ncost: 329406.8308592081
Run: 10, iteration: 34/50, moves: 18, ncost: 329406.7813625913
Run: 10, iteration: 35/50, moves: 4, ncost: 329406.7756372554
Run: 10, iteration: 36/50, moves: 0, ncost: 329406.7756372554
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 11, iteration: 1/50, moves: 32606, ncost: 338019.6824556815
Run: 11, iteration: 2/50, moves: 6681, ncost: 333326.6337246361
Run: 11, iteration: 3/50, moves: 3065, ncost: 331757.11856943776
Run: 11, iteration: 4/50, moves: 2121, ncost: 330993.2385649948
Run: 11, iteration: 5/50, moves: 1688, ncost: 330456.28692060855
Run: 11, iteration: 6/50, moves: 1347, ncost: 330078.66284686973
Run: 11, iteration: 7/50, moves: 1156, ncost: 329832.1164140871
Run: 11, iteration: 8/50, moves: 976, ncost: 329668.233681285
Run: 11, iteration: 9/50, moves: 757, ncost: 329566.10229903134
Run: 11, iteration: 10/50, moves: 580, ncost: 329511.01567630953
Run: 11, iteration: 11/50, moves: 414, ncost: 329487.1109180923
Run: 11, iteration: 12/50, moves: 271, ncost: 329475.228121823
Run: 11, iteration: 13/50, moves: 266, ncost: 329462.3529683459
Run: 11, iteration: 14/50, moves: 360, ncost: 329443.93559070927
Run: 11, iteration: 15/50, moves: 286, ncost: 329432.619341048
Run: 11, iteration: 16/50, moves: 229, ncost: 329425.095946743
Run: 11, iteration: 17/50, moves: 158, ncost: 329421.09644901875
Run: 11, iteration: 18/50, moves: 120, ncost: 329418.4857377501
Run: 11, iteration: 19/50, moves: 94, ncost: 329417.13191228564
Run: 11, iteration: 20/50, moves: 52, ncost: 329416.74004029087
Run: 11, iteration: 21/50, moves: 45, ncost: 329416.26802756725
Run: 11, iteration: 22/50, moves: 59, ncost: 329415.5324978194
Run: 11, iteration: 23/50, moves: 59, ncost: 329414.90654766955
Run: 11, iteration: 24/50, moves: 67, ncost: 329414.0476682617
Run: 11, iteration: 25/50, moves: 66, ncost: 329413.26543484896
Run: 11, iteration: 26/50, moves: 57, ncost: 329412.71017489495
Run: 11, iteration: 27/50, moves: 94, ncost: 329410.611293928
Run: 11, iteration: 28/50, moves: 117, ncost: 329408.46353463916
Run: 11, iteration: 29/50, moves: 73, ncost: 329407.6145386551
Run: 11, iteration: 30/50, moves: 45, ncost: 329407.2232227668
Run: 11, iteration: 31/50, moves: 32, ncost: 329407.03345388797
Run: 11, iteration: 32/50, moves: 40, ncost: 329406.83085920813
Run: 11, iteration: 33/50, moves: 18, ncost: 329406.7813625913
Run: 11, iteration: 34/50, moves: 4, ncost: 329406.7756372551
Run: 11, iteration: 35/50, moves: 0, ncost: 329406.7756372551
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 12, iteration: 1/50, moves: 13255, ncost: 333941.76490359666
Run: 12, iteration: 2/50, moves: 4626, ncost: 332011.0607974965
Run: 12, iteration: 3/50, moves: 2034, ncost: 331224.1695276123
Run: 12, iteration: 4/50, moves: 1267, ncost: 330986.26335662726
Run: 12, iteration: 5/50, moves: 683, ncost: 330909.03691375256
Run: 12, iteration: 6/50, moves: 396, ncost: 330882.9698964935
Run: 12, iteration: 7/50, moves: 234, ncost: 330873.987839557
Run: 12, iteration: 8/50, moves: 114, ncost: 330871.2693233985
Run: 12, iteration: 9/50, moves: 48, ncost: 330870.7428313399
Run: 12, iteration: 10/50, moves: 26, ncost: 330870.57537243323
Run: 12, iteration: 11/50, moves: 16, ncost: 330870.5214017958
Run: 12, iteration: 12/50, moves: 11, ncost: 330870.4655271171
Run: 12, iteration: 13/50, moves: 8, ncost: 330870.42876700533
Run: 12, iteration: 14/50, moves: 6, ncost: 330870.4025289203
Run: 12, iteration: 15/50, moves: 1, ncost: 330870.40072360105
Run: 12, iteration: 16/50, moves: 0, ncost: 330870.40072360105
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 13, iteration: 1/50, moves: 22065, ncost: 337516.8248952934
Run: 13, iteration: 2/50, moves: 5748, ncost: 332218.473318809
Run: 13, iteration: 3/50, moves: 2455, ncost: 331173.95442265953
Run: 13, iteration: 4/50, moves: 1017, ncost: 330945.2509053001
Run: 13, iteration: 5/50, moves: 496, ncost: 330890.6217795961
Run: 13, iteration: 6/50, moves: 252, ncost: 330877.55850255623
Run: 13, iteration: 7/50, moves: 126, ncost: 330873.1671523429
Run: 13, iteration: 8/50, moves: 75, ncost: 330871.2104535868
Run: 13, iteration: 9/50, moves: 42, ncost: 330870.71343891596
Run: 13, iteration: 10/50, moves: 25, ncost: 330870.52711509774
Run: 13, iteration: 11/50, moves: 18, ncost: 330870.4438646221
Run: 13, iteration: 12/50, moves: 7, ncost: 330870.43199364643
Run: 13, iteration: 13/50, moves: 5, ncost: 330870.42354953074
Run: 13, iteration: 14/50, moves: 0, ncost: 330870.42354953074
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 14, iteration: 1/50, moves: 27818, ncost: 346091.39717139787
Run: 14, iteration: 2/50, moves: 6766, ncost: 340994.7008944515
Run: 14, iteration: 3/50, moves: 3409, ncost: 339042.3321360135
Run: 14, iteration: 4/50, moves: 3060, ncost: 337695.9868959047
Run: 14, iteration: 5/50, moves: 3605, ncost: 335502.5293387175
Run: 14, iteration: 6/50, moves: 3581, ncost: 333634.6769040707
Run: 14, iteration: 7/50, moves: 3040, ncost: 332098.6893820456
Run: 14, iteration: 8/50, moves: 2350, ncost: 331235.3819110435
Run: 14, iteration: 9/50, moves: 1726, ncost: 330713.5156592098
Run: 14, iteration: 10/50, moves: 1384, ncost: 330271.0993506366
Run: 14, iteration: 11/50, moves: 1265, ncost: 329961.7607449689
Run: 14, iteration: 12/50, moves: 1094, ncost: 329751.55353500554
Run: 14, iteration: 13/50, moves: 891, ncost: 329616.2909962701
Run: 14, iteration: 14/50, moves: 729, ncost: 329530.7126512566
Run: 14, iteration: 15/50, moves: 515, ncost: 329496.28148689296
Run: 14, iteration: 16/50, moves: 319, ncost: 329479.70318719785
Run: 14, iteration: 17/50, moves: 247, ncost: 329469.48111102125
Run: 14, iteration: 18/50, moves: 296, ncost: 329453.7742080854
Run: 14, iteration: 19/50, moves: 298, ncost: 329439.2411591536
Run: 14, iteration: 20/50, moves: 270, ncost: 329429.14760876127
Run: 14, iteration: 21/50, moves: 200, ncost: 329423.26265908324
Run: 14, iteration: 22/50, moves: 140, ncost: 329420.01355852647
Run: 14, iteration: 23/50, moves: 106, ncost: 329417.9091370084
Run: 14, iteration: 24/50, moves: 81, ncost: 329416.96074809914
Run: 14, iteration: 25/50, moves: 47, ncost: 329416.6261214159
Run: 14, iteration: 26/50, moves: 57, ncost: 329415.9226996333
Run: 14, iteration: 27/50, moves: 54, ncost: 329415.2902361125
Run: 14, iteration: 28/50, moves: 61, ncost: 329414.6351341191
Run: 14, iteration: 29/50, moves: 78, ncost: 329413.6307400134
Run: 14, iteration: 30/50, moves: 67, ncost: 329412.92229496565
Run: 14, iteration: 31/50, moves: 50, ncost: 329412.43167294975
Run: 14, iteration: 32/50, moves: 118, ncost: 329409.5008635875
Run: 14, iteration: 33/50, moves: 94, ncost: 329408.07712416735
Run: 14, iteration: 34/50, moves: 54, ncost: 329407.54439742956
Run: 14, iteration: 35/50, moves: 45, ncost: 329407.15138154564
Run: 14, iteration: 36/50, moves: 41, ncost: 329406.9401624331
Run: 14, iteration: 37/50, moves: 33, ncost: 329406.7913684379
Run: 14, iteration: 38/50, moves: 9, ncost: 329406.776793438
Run: 14, iteration: 39/50, moves: 0, ncost: 329406.776793438
Init: initializing centroids
Init: initializing clusters
Starting iterations...
Run: 15, iteration: 1/50, moves: 15373, ncost: 360790.2971112735
Run: 15, iteration: 2/50, moves: 6879, ncost: 356097.04691125295
Run: 15, iteration: 3/50, moves: 4116, ncost: 352034.20162928314
Run: 15, iteration: 4/50, moves: 4687, ncost: 346182.51807123586
Run: 15, iteration: 5/50, moves: 4759, ncost: 339551.6079500333
Run: 15, iteration: 6/50, moves: 5044, ncost: 334145.25598358613
Run: 15, iteration: 7/50, moves: 3233, ncost: 331955.57934890833
Run: 15, iteration: 8/50, moves: 1986, ncost: 331174.90519809036
Run: 15, iteration: 9/50, moves: 1031, ncost: 330961.6422965072
Run: 15, iteration: 10/50, moves: 566, ncost: 330896.1132909785
Run: 15, iteration: 11/50, moves: 318, ncost: 330877.4115637081
Run: 15, iteration: 12/50, moves: 167, ncost: 330871.8621011546
Run: 15, iteration: 13/50, moves: 63, ncost: 330870.79051164014
Run: 15, iteration: 14/50, moves: 24, ncost: 330870.63117846864
Run: 15, iteration: 15/50, moves: 14, ncost: 330870.5446469182
Run: 15, iteration: 16/50, moves: 23, ncost: 330870.41957646795
Run: 15, iteration: 17/50, moves: 9, ncost: 330870.37586539995
Run: 15, iteration: 18/50, moves: 5, ncost: 330870.365884452
Run: 15, iteration: 19/50, moves: 1, ncost: 330870.3650780018
Run: 15, iteration: 20/50, moves: 0, ncost: 330870.3650780018
Best run was number 2

# Print cluster centroids of the trained model.
print(k_proto.cluster_centroids_)

[['-0.026474219423090733' '0.9270988108485877' '-0.23202513323310273'
  '-0.6485379695241148' '-0.49503928066593206' '-0.636223914836607'
  '-0.6619409334218479' '0.9264933475219304' '0.9238721302369118'
  'Travel Agent/Operator' 'Other' 'Europe']
 ['0.19655852227956094' '0.7485119297437397' '0.27666755735969256'
  '0.4245679362905632' '0.6920935420081291' '0.8321333052517851'
  '0.7816457156368039' '0.7461077330740551' '0.7524292586507885'
  'Travel Agent/Operator' 'Other' 'Europe']
 ['-0.11813409773831288' '-0.833980898532122' '0.13222324007531272'
  '1.3292120368016815' '0.8887509164296602' '1.0815161438956529'
  '1.1191601051855908' '-0.8357187970010412' '-0.82800699069321'
  'Travel Agent/Operator' 'Other' 'Europe']
 ['-0.04729913705966276' '-0.8740579230374662' '-0.04769458925786531'
  '-0.3830663690531079' '-0.4712116309702116' '-0.5376728589263355'
  '-0.5011518088636748' '-0.8710910775435204' '-0.8768297963777862'
  'Travel Agent/Operator' 'Other' 'Europe']]

# Print training statistics
print(k_proto.cost_)
print(k_proto.n_iter_)

327952.3292628051
22

#Print count of each cluster
print(pd.Series(clusters).value_counts())

3    21105
0    18182
1    13855
2    10528
dtype: int64

proto_labs = k_proto.labels_
proto_labs

array([0, 1, 1, ..., 0, 0, 0], dtype=uint16)

cluster_dict = []
for c in clusters:
  cluster_dict.append(c)

df.head()

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	PersonsNights	RoomNights	DaysSinceLastStay	DaysSinceFirstStay	DistributionChannel	MarketSegment	Continent_Name	cluster
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	0.591825	1.389564	-1.261711	1.578742	Corporate	Corporate	Europe	2
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	1.154189	1.389564	1.584588	1.578742	Travel Agent/Operator	Travel Agent/Operator	Europe	1
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	1.154189	1.389564	1.584588	1.578742	Travel Agent/Operator	Travel Agent/Operator	Europe	1
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	-0.532903	-0.675838	1.584588	1.578742	Travel Agent/Operator	Other	Asia	0
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	1.154189	1.389564	1.584588	1.578742	Travel Agent/Operator	Other	Europe	1

df['cluster'] = cluster_dict

plt.subplots(figsize = (10,6))
sns.countplot(x=df['MarketSegment'],order=df['MarketSegment'].value_counts().index,hue=df['cluster'])
plt.show()

plt.subplots(figsize = (10,6))
sns.countplot(x=df['Continent_Name'],order=df['Continent_Name'].value_counts().index,hue=df['cluster'])
plt.show()

plt.subplots(figsize = (10,6))
sns.countplot(x=df['DistributionChannel'],order=df['DistributionChannel'].value_counts().index,hue=df['cluster'])
plt.show()

EXPECTATION & MAXIMIZATION CLUSTERING

import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline


plt.style.use('seaborn')

data=pd.read_csv('processed_data.csv')

data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
       'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
       'DaysSinceFirstStay']]

import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline


plt.style.use('seaborn')

data=pd.read_csv('processed_data.csv')

data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
       'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
       'DaysSinceFirstStay']]

# Dimensionality reduction

from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, silhouette_samples
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans, DBSCAN, MeanShift, estimate_bandwidth, AgglomerativeClustering
from sklearn.mixture import GaussianMixture

# Initialize a new PCA model with a default number of components.
pca = PCA()
pca.fit(data1)


explained_variance = pca.explained_variance_

# Plot explained variance
fig = plt.figure(figsize=(7,7))
plt.plot(np.arange(1,10,1), explained_variance)
plt.xlabel('N of components', fontsize=13)
plt.ylabel('Explained variance', fontsize=13)
plt.xticks([0,5,10,15,20,40,60,80])
plt.show()

pca.explained_variance_ratio_

array([3.25651506e-01, 2.37407925e-01, 1.52562020e-01, 1.25313867e-01,
       8.01549678e-02, 5.01002681e-02, 2.77228757e-02, 1.05215468e-03,
       3.44155412e-05])

#Scree plot

import numpy as np
fig, ax = plt.subplots(figsize=(12, 8))

num_components = pca.n_components_
idx = np.arange(num_components)
vals = pca.explained_variance_ratio_*100

ax.plot(idx, vals, color='blue')
ax.scatter(idx, vals, color='blue', s=50)

for i in range(9):
    ax.annotate(r"{:2.2f}%".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))

ax.set_title('Scree Plot')
ax.set_xlabel('Principal Components')
ax.set_ylabel('% Variance Explained')

ax.set_xticklabels(idx+1, fontsize=12)
ax.set_xticks(np.arange(9))
ax.set_xlim(left=0-0.5, right=8+0.5)
ax.set_ylim(top=max(vals)+5)

(-1.624643895711861, 37.56515055083923)

pca = PCA().fit(data1)

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)

fig, ax = plt.subplots()
xi = np.arange(1, 10, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
print(y)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')

plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')

plt.axhline(y=0.99, color='r', linestyle='--')
plt.axvline(x=7, color='r', linestyle='--')
plt.text(0.5, 0.85, '90% cut-off threshold', color = 'red', fontsize=16)
plt.text(0.5, 0.70, 'n_components=7', color = 'black', fontsize=14)
ax.grid(axis='x')
plt.show()

[0.32565151 0.56305943 0.71562145 0.84093532 0.92109029 0.97119055
 0.99891343 0.99996558 1.        ]

pca = PCA(n_components=7)
x_7cols = pca.fit_transform(data1)

# BIC for GMM
from sklearn.mixture import GaussianMixture
n_components = range(1, 30)
covariance_type = [ 'spherical', 'tied', 'diag', 'full']
spherical_score=[]
tied_score=[]
diag_score=[]
full_score=[]
for cov in covariance_type:
    for n_comp in n_components:
        gmm=GaussianMixture(n_components=n_comp,covariance_type=cov)
        gmm.fit(x_7cols)
        if cov=='spherical':
            spherical_score.append((gmm.bic(x_7cols)))
        elif cov=='tied':
            tied_score.append((gmm.bic(x_7cols)))
        elif cov=='diag':
            diag_score.append((gmm.bic(x_7cols)))
        elif cov=='full':
            full_score.append((gmm.bic(x_7cols)))

x=list(range(1,30))

fig, axs = plt.subplots(2,2,figsize=(10,10))
axs[0,0].plot(x,spherical_score,color='red')
axs[0,0].title.set_text('cov_type :Spherical')

axs[0,1].plot(x,tied_score,color='blue')
axs[0,1].title.set_text('cov_type : Tied')

axs[1,0].plot(x,diag_score,color='green')
axs[1,0].title.set_text('cov_type :Diag')

axs[1,1].plot(x,full_score,color='black')
axs[1,1].title.set_text('cov_type : Full')

fig.suptitle('BIC score', fontsize=14)
#plt.legend()
plt.show()

n_components = np.arange(1, 10)
clfs = [GaussianMixture(n, max_iter = 1000).fit(x_7cols) for n in n_components]
bics = [clf.bic(x_7cols) for clf in clfs]
aics = [clf.aic(x_7cols) for clf in clfs]

fig,axs=plt.subplots(1,2,figsize=(10,6))

axs[0].plot(n_components, bics, label = 'BIC',color='red')
axs[0].set_xlabel('n_components')
axs[0].set_ylabel('BIC SCORE')
axs[1].plot(n_components, aics, label = 'AIC')
axs[1].set_xlabel('n_components')
axs[1].set_ylabel('AIC SCORE')
fig.suptitle(' AIC & BIC score', fontsize=14)
plt.legend()
plt.show()

# Initializes and predicts labels
gm_labels = GaussianMixture(n_components=4, covariance_type='full').fit_predict(x_7cols)
# Calculates average silhouette coefficient
silhouette_gm = silhouette_score(x_7cols, gm_labels)
# Creates dataframe of the labels for later use
gaussmix_df = pd.DataFrame(gm_labels, columns=['cluster_id'])

gaussmix_df['cluster_id'].unique()

array([1, 3, 0, 2], dtype=int64)

print('The Silhouette coefficient for the EM Clustering algorithm is '+str(round(silhouette_gm, 2)))

# Creates plot of cluster sizes
gaussmix_df.value_counts().sort_index().plot.bar(figsize=(7, 6))
plt.title('Expectation Maximization clusters size distribution', fontsize=14)
plt.xlabel('Cluster', fontsize=13)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','k 4'], rotation=0)
plt.show()

The Silhouette coefficient for the EM Clustering algorithm is 0.09

df_k = data1.copy(deep=True)
df_k['label'] = gm_labels
df_k

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	PersonsNights	RoomNights	DaysSinceLastStay	DaysSinceFirstStay	label
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	4	2	-1.261711	1.578742	1
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	4	2	1.584588	1.578742	3
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	4	2	1.584588	1.578742	3
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	3	1	1.584588	1.578742	0
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	4	2	1.584588	1.578742	3
...	...	...	...	...	...	...	...	...	...	...
63665	0.041387	-1.547764	-0.733896	-0.778844	0.944011	3	1	-1.543988	-1.548269	0
63666	0.115001	-1.547764	0.044904	-0.914618	-1.030075	2	0	-1.543988	-1.548269	0
63667	0.335844	-1.547764	0.319129	-0.116844	0.362986	4	2	-1.543988	-1.548269	3
63668	-0.032227	-1.547764	0.242346	-0.157165	2.666086	4	2	-1.543988	-1.548269	3
63669	1.808129	-1.547764	0.242346	2.534449	-0.988073	4	2	-1.543988	-1.548269	3

63670 rows × 10 columns

plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df_k['AverageLeadTime'],df_k['LodgingRevenue'],hue=df_k['label'],palette="tab10")
plt.legend()
plt.show()

PERFORMANCE METRICS

from sklearn import metrics
from sklearn.metrics import davies_bouldin_score

#SILHOUETTE SCORE

metrics.silhouette_score(x_7cols, gm_labels, metric='euclidean')

0.08758197037444428

#Calinski-Harabasz Index

metrics.calinski_harabasz_score(x_7cols, gm_labels)

6031.089723494116

#Davies-Bouldin Index

#AFTER PCA
davies_bouldin_score(x_7cols, gm_labels)

2.6315988821797385

DBSCAN CLUSTERING

import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import matplotlib
  
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import normalize
from sklearn.decomposition import PCA

# Importing the dataset
X1 = pd.read_csv("processed_data.csv")
X1.head()

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	BookingsCanceled	BookingsNoShowed	BookingsCheckedIn	PersonsNights	RoomNights	...	MarketSegment_Direct	MarketSegment_Groups	MarketSegment_Other	MarketSegment_Travel Agent/Operator	Continent_Name_Antarctica	Continent_Name_Asia	Continent_Name_Europe	Continent_Name_North America	Continent_Name_Oceania	Continent_Name_South America
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	0	0	0.0	4	2	...	0	0	0	0	0	0	1	0	0	0
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	0	0	0.0	4	2	...	0	0	0	1	0	0	1	0	0	0
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	0	0	0.0	4	2	...	0	0	0	1	0	0	1	0	0	0
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	0	0	0.0	3	1	...	0	0	1	0	0	1	0	0	0	0
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	0	0	0.0	4	2	...	0	0	1	0	0	0	1	0	0	0

5 rows × 28 columns

X=X1.iloc[:,0:5] # SCALED CONTIUOUS VARIABLE
X.head()

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue
0	0.262230	-1.265417	-0.459671	0.023456	0.360185
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046
2	0.924758	1.581582	0.066842	-0.515525	-0.274042
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064
4	-1.136441	1.581582	-0.536454	0.698211	0.201979

X.shape

(63670, 5)

pca1 = PCA(n_components=4).fit_transform(X)

#with pca components

# eps = 0.6, min_samples = 10 gives 4  clusters and silhoutte score is 0.25875075478737775

# Numpy array of all the cluster labels assigned to each data point
db_default = DBSCAN(eps = 0.6, min_samples = 10).fit(pca1)
labels1 = db_default.labels_
labels1

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels1)) - (1 if -1 in labels1 else 0)
n_noise_ = list(labels1).count(-1)

print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)

Estimated number of clusters: 4
Estimated number of noise points: 101

from sklearn import metrics

print(metrics.silhouette_score(pca1,labels1))

0.25875075478737775

df1 = X.copy(deep=True)
df1['label'] = labels1
df1

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	label
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	0
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	0
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	0
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	0
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	0
...	...	...	...	...	...	...
63665	0.041387	-1.547764	-0.733896	-0.778844	0.944011	0
63666	0.115001	-1.547764	0.044904	-0.914618	-1.030075	0
63667	0.335844	-1.547764	0.319129	-0.116844	0.362986	0
63668	-0.032227	-1.547764	0.242346	-0.157165	2.666086	0
63669	1.808129	-1.547764	0.242346	2.534449	-0.988073	0

63670 rows × 6 columns

import seaborn as sns
sns.pairplot(df1)

<seaborn.axisgrid.PairGrid at 0x1c5029ca4c0>

import seaborn as sns
import warnings
warnings.filterwarnings("ignore")

plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df1['AverageLeadTime'],df1['LodgingRevenue'],hue=df1['label'],palette="tab10")
plt.legend()
plt.show()

# eps = 0.9, min_samples = 20 gives 2 clusters and silhoutte score is 0.43359443420521376

db_default = DBSCAN(eps = 0.9, min_samples =20).fit(pca1)
labels2 = db_default.labels_
labels2 

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels2)) - (1 if -1 in labels2 else 0)
n_noise_ = list(labels2).count(-1)

print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)

Estimated number of clusters: 2
Estimated number of noise points: 27

from sklearn import metrics

print(metrics.silhouette_score(pca1,labels2))

0.43359443420521376

df2 = X.copy(deep=True)
df2['label'] = labels2
df2

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	label
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	0
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	0
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	0
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	0
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	0
...	...	...	...	...	...	...
63665	0.041387	-1.547764	-0.733896	-0.778844	0.944011	0
63666	0.115001	-1.547764	0.044904	-0.914618	-1.030075	0
63667	0.335844	-1.547764	0.319129	-0.116844	0.362986	0
63668	-0.032227	-1.547764	0.242346	-0.157165	2.666086	0
63669	1.808129	-1.547764	0.242346	2.534449	-0.988073	0

63670 rows × 6 columns

plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df2['AverageLeadTime'],df2['LodgingRevenue'],hue=df2['label'])
plt.legend()
plt.show()

#without pca components

db_default = DBSCAN(eps = 0.9, min_samples = 10).fit(X)
labels3 = db_default.labels_
labels3

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels3)) - (1 if -1 in labels3 else 0)
n_noise_ = list(labels3).count(-1)

print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)

Estimated number of clusters: 2
Estimated number of noise points: 49

from sklearn import metrics

print(metrics.silhouette_score(X,labels3))

0.3901207693250093

df3 = X.copy(deep=True)
df3['label'] = labels3
df3

	Age	DaysSinceCreation	AverageLeadTime	LodgingRevenue	OtherRevenue	label
0	0.262230	-1.265417	-0.459671	0.023456	0.360185	0
1	0.262230	1.581582	-0.284167	-0.350951	-0.372046	0
2	0.924758	1.581582	0.066842	-0.515525	-0.274042	0
3	0.483072	1.581582	-0.317074	-0.556669	-0.778064	0
4	-1.136441	1.581582	-0.536454	0.698211	0.201979	0
...	...	...	...	...	...	...
63665	0.041387	-1.547764	-0.733896	-0.778844	0.944011	0
63666	0.115001	-1.547764	0.044904	-0.914618	-1.030075	0
63667	0.335844	-1.547764	0.319129	-0.116844	0.362986	0
63668	-0.032227	-1.547764	0.242346	-0.157165	2.666086	0
63669	1.808129	-1.547764	0.242346	2.534449	-0.988073	0

63670 rows × 6 columns

plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df3['AverageLeadTime'],df3['LodgingRevenue'],hue=df3['label'],palette="tab10")
plt.legend()
plt.show()

from sklearn.metrics import accuracy_score
#accuracy_score(y, y_cluster)

from sklearn.tree import DecisionTreeClassifier
X = df2.drop(['label'], axis=1)
y = df2['label']

from sklearn.model_selection import train_test_split
# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)

model1 = DecisionTreeClassifier()
model1 = model1.fit(X_train, y_train)

ypred = model1.predict(X_test)

accuracy_score(y_test, ypred)

0.9997382336003351

from sklearn.ensemble import BaggingClassifier

model2 = BaggingClassifier()
model2 = model2.fit(X_train, y_train)

from sklearn import metrics
from sklearn.metrics import davies_bouldin_score

ypred = model2.predict(X_test)

accuracy_score(y_test, ypred)

0.9996858803204021

from sklearn.metrics import accuracy_score

from sklearn.tree import DecisionTreeClassifier
X = df1.drop(['label'], axis=1)
y = df1['label']

from sklearn.model_selection import train_test_split
# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)

model1 = DecisionTreeClassifier()
model1 = model1.fit(X_train, y_train)

ypred = model1.predict(X_test)

accuracy_score(y_test, ypred)

0.9986911680016753

from sklearn.ensemble import BaggingClassifier

model2 = BaggingClassifier()
model2 = model2.fit(X_train, y_train)

from sklearn import metrics
from sklearn.metrics import davies_bouldin_score

ypred = model2.predict(X_test)

accuracy_score(y_test, ypred)

0.9991099942411392

AGGLOMERATIVE CLUSTERING

import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline


plt.style.use('seaborn')

data=pd.read_csv('processed_data.csv')

data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
       'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
       'DaysSinceFirstStay']]

import pandas as pd
import numpy as np
from warnings import filterwarnings
filterwarnings('ignore')
import klib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline


plt.style.use('seaborn')

data=pd.read_csv('processed_data.csv')

data1=data[['Age', 'DaysSinceCreation', 'AverageLeadTime', 'LodgingRevenue',
       'OtherRevenue','PersonsNights', 'RoomNights', 'DaysSinceLastStay',
       'DaysSinceFirstStay']]

# Dimensionality reduction

from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, silhouette_samples
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans, DBSCAN, MeanShift, estimate_bandwidth, AgglomerativeClustering
from sklearn.mixture import GaussianMixture

# Initialize a new PCA model with a default number of components.
pca = PCA()
pca.fit(data1)


explained_variance = pca.explained_variance_

# Plot explained variance
fig = plt.figure(figsize=(7,7))
plt.plot(np.arange(1,10,1), explained_variance)
plt.xlabel('N of components', fontsize=13)
plt.ylabel('Explained variance', fontsize=13)
plt.xticks([0,5,10,15,20,40,60,80])
plt.show()

pca.explained_variance_ratio_

array([3.25651506e-01, 2.37407925e-01, 1.52562020e-01, 1.25313867e-01,
       8.01549678e-02, 5.01002681e-02, 2.77228757e-02, 1.05215468e-03,
       3.44155412e-05])

#Scree plot

import numpy as np
fig, ax = plt.subplots(figsize=(12, 8))

num_components = pca.n_components_
idx = np.arange(num_components)
vals = pca.explained_variance_ratio_*100

ax.plot(idx, vals, color='blue')
ax.scatter(idx, vals, color='blue', s=50)

for i in range(9):
    ax.annotate(r"{:2.2f}%".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))

ax.set_title('Scree Plot')
ax.set_xlabel('Principal Components')
ax.set_ylabel('% Variance Explained')

ax.set_xticklabels(idx+1, fontsize=12)
ax.set_xticks(np.arange(9))
ax.set_xlim(left=0-0.5, right=8+0.5)
ax.set_ylim(top=max(vals)+5)

(-1.6246438957118616, 37.56515055083924)

pca = PCA().fit(data1)

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)

fig, ax = plt.subplots()
xi = np.arange(1, 10, step=1)
y = np.cumsum(pca.explained_variance_ratio_)
print(y)
plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')

plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 11, step=1)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')

plt.axhline(y=0.99, color='r', linestyle='--')
plt.axvline(x=7, color='r', linestyle='--')
plt.text(0.5, 0.85, '90% cut-off threshold', color = 'red', fontsize=16)
plt.text(0.5, 0.70, 'n_components=7', color = 'black', fontsize=14)
ax.grid(axis='x')
plt.show()

[0.32565151 0.56305943 0.71562145 0.84093532 0.92109029 0.97119055
 0.99891343 0.99996558 1.        ]

pca = PCA(n_components=7)
x_7cols = pca.fit_transform(data1)

from scipy.cluster.hierarchy import linkage, dendrogram
plt.figure(figsize=[10,10])
merg = linkage(x_7cols, method='ward')
dendrogram(merg, leaf_rotation=90)
plt.title('Dendrogram')
plt.xlabel('Data Points')
plt.ylabel('Euclidean Distances')
plt.show()

# Initializes and predicts labels
ac_labels = AgglomerativeClustering(n_clusters=4, linkage='ward').fit_predict(x_7cols)
# Calculates average silhouette coefficient
silhouette_ac = silhouette_score(x_7cols, ac_labels)
# Creates dataframe of the labels for later use
agglo_df = pd.DataFrame(ac_labels, columns=['cluster_id'])

print('The Silhouette coefficient for the Agglomerative Hierarchical Clustering algorithm is '+str(round(silhouette_ac, 2)))

# Creates plot of cluster sizes
agglo_df.value_counts().sort_index().plot.bar(figsize=(7, 6))
plt.title('Agglomerative clusters size distribution', fontsize=14)
plt.xlabel('Cluster', fontsize=13)
plt.ylabel('N of data points', fontsize=13)
plt.xticks(ticks=[0,1,2,3], labels=['K 1','K 2','K 3','k 4'], rotation=0)
plt.show()

The Silhouette coefficient for the Agglomerative Hierarchical Clustering algorithm is 0.2

unscaled_data=pd.read_csv('Before_scaling_data.csv')

df = unscaled_data.copy(deep=True)
df['label'] = ac_labels

unscaled_data['PersonsNights'].unique()

array([4, 3, 1, 0, 2])

plt_1=plt.figure(figsize=(15,15))
sns.scatterplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],palette="tab10")
plt.legend()
plt.show()

plt_1=plt.figure(figsize=(15,25))
sns.relplot(df['AverageLeadTime'],df['LodgingRevenue'],hue=df['label'],
            col=df['PersonsNights'],palette="tab10",col_wrap=2)

plt.show()

<Figure size 1080x1800 with 0 Axes>

PERFORMANCE METRICS

from sklearn import metrics
from sklearn.metrics import davies_bouldin_score

#SILHOUETTE SCORE

metrics.silhouette_score(x_7cols, ac_labels, metric='euclidean')

0.19836000986864044

#Calinski-Harabasz Index

metrics.calinski_harabasz_score(x_7cols, ac_labels)

13938.022724703173

#Davies-Bouldin Index

davies_bouldin_score(x_7cols, ac_labels)

1.495576493288908